Liquid droplet ejection device and liquid droplet ejection method

Abstract

A liquid droplet ejection method is provided including capturing an image of a pattern on a substrate, acquiring first image data corresponding to the pattern, and applying the first image data to a machine learning model to perform machine learning and generate liquid droplet ejection conditions based on an electrostatic method. The liquid droplet ejection method described above may further include generating the machine learning model by performing machine learning on pre-acquired first image data.

1 . A liquid droplet ejection method comprising:
 capturing an image of a pattern on a substrate;   acquiring first image data corresponding to the pattern; and   applying the first image data to a machine learning model to perform machine learning and generate liquid droplet ejection conditions based on an electrostatic method.   
     
     
         2 . The liquid droplet ejection method according to  claim 1 , further comprising:
 generating the machine learning model by performing machine learning on pre-acquired first image data.   
     
     
         3 . The liquid droplet ejection method according to  claim 1 , further comprising:
 capturing an image of a shape of a liquid droplet ejected from an electrostatic liquid droplet ejection nozzle, wherein   the machine learning model is a learning model trained on a correlation between the pattern and the shape the liquid droplet.   
     
     
         4 . The liquid droplet ejection method according to  claim 1 , wherein
 the machine learning model is a learning model trained on a correlation between the pattern and conductivity of the substrate.   
     
     
         5 . The liquid droplet ejection method according to  claim 1 , wherein
 the machine learning model is a learning model trained on a correlation between the pattern and at least one of a shape of the electrostatic liquid droplet ejection nozzle and humidity.   
     
     
         6 . The liquid droplet ejection method according to  claim 1 , wherein
 the machine learning model is a learning model trained on a correlation between the pattern and a contact angle of a liquid droplet with respect to the substrate.   
     
     
         7 . The liquid droplet ejection method according to  claim 1 , wherein
 the liquid droplet ejection conditions include at least one of an ejection voltage, frequency, and a distance between the substrate and the electrostatic liquid droplet ejection nozzle.   
     
     
         8 . A liquid droplet ejection device comprising:
 an imaging unit configured to capture an image of a pattern on a substrate; and   a control unit configured to acquire first image data corresponding to the pattern, apply the first image data to a machine learning model to perform machine learning, and generate liquid droplet ejection conditions based on an electrostatic method.   
     
     
         9 . The liquid droplet ejection device according to  claim 8 , wherein
 the control unit generates the machine learning model by performing machine learning on pre-acquired first image data.   
     
     
         10 . The liquid droplet ejection device according to  claim 8 , further comprising:
 a second imaging unit configured to capture an image of a shape of a liquid droplet ejected from an electrostatic liquid droplet ejection nozzle, wherein   the machine learning model is a learning model trained on a correlation between the pattern and the shape of the liquid droplet.   
     
     
         11 . The liquid droplet ejection device according to  claim 8 , wherein
 the machine learning model is a learning model trained on a correlation between the pattern and conductivity of the substrate.   
     
     
         12 . The liquid droplet ejection device according to  claim 8 , wherein
 the machine learning model is a learning model trained on a correlation between the pattern and at least one of a shape of the electrostatic liquid droplet ejection nozzle and humidity.   
     
     
         13 . The liquid droplet ejection device according to  claim 8 , wherein
 the machine learning model is a learning model trained on a correlation between the pattern and a contact angle of a liquid droplet with respect to the substrate.   
     
     
         14 . The liquid droplet ejection device according to  claim 8 , wherein
 the liquid droplet ejection conditions include at least one of an ejection voltage, frequency, and a distance between the substrate and the electrostatic liquid droplet ejection nozzle.